电动汽车实际运营时，动力电池健康状态的动态变化直接关系到行驶安全和续航里程的可靠性。复杂路况与环境温度波动以及随机充放电行为的叠加，让传统监测方法很难精准抓住电池老化时那些不显著的特征。借助计算机深度学习的非线性拟合能力和深层特征挖掘能力，可以从大量运行数据中建立起健康状态和多维度参数的关联。通过优化模型结构与筛选输入特征，可以提升不同工况下的预测适配度，减少数据不足或者噪声带来的偏差。这在一定程度上能够为电池管理系统的智能化升级提供技术支持，也能为电池梯次利用的合理规划提供参考。

During the actual operation of electric vehicles, the dynamic changes in the health status of power batteries are directly related to driving safety and the reliability of driving range. The superposition of complex road conditions, fluctuations in ambient temperature, and random charging and discharging behaviors makes it difficult for traditional monitoring methods to accurately capture the subtle characteristics of battery aging. By leveraging the nonlinear fitting capabilities and deep feature extraction abilities of computer deep learning, correlations between health status and multidimensional parameters can be established from vast amounts of operational data. Through optimizing model architecture and selecting input features, prediction adaptability under various operating conditions can be improved, reducing deviations caused by insufficient data or noise. This provides technical support for the intelligent upgrade of battery management systems and offers references for the rational planning of battery cascade utilization to a certain extent.